Image processing apparatus, image processing method, and non-transitory computer readable medium storing image processing program

ABSTRACT

An object is to provide an image processing apparatus capable of appropriately distinguishing various object types. An image processing apparatus ( 1 C) comprising: detector means ( 11 ) for detecting objects in an input SAR image and generating object chips; projection calculator means ( 12 ) for calculating projection information of each object using SAR geometry; feature learner means ( 14 ) for learning, for each object, a relation between an object chip and its projection information, and thereby generating learnt features of object chips; and classifier means ( 15 ) for classifying object chips into classes based on the learnt features of object chips.

TECHNICAL FIELD

The present disclosure relates to an image processing apparatus, animage processing method, and a non-transitory computer readable mediumstoring an image processing program. In particular, the presentdisclosure relates to an image processing apparatus, an image processingmethod, and a non-transitory computer readable medium storing an imageprocessing program for making object classification in an SAR (SyntheticAperture Radar) image.

BACKGROUND ART

Automatic Target Recognition (ATR) refers to the use of computerprograms and algorithms to detect and recognize target signatures usingsensor data. Target recognition algorithms have numerous applications infields such as defense where the objective is to detect and recognize anobject of interest even from a noisy environment.

To deploy target recognition at a large scale, Synthetic Aperture Radar(SAR) is a promising technology. SAR is an active high resolutionimaging radar which offers all weather and day-and-night monitoring ofearth's surface unlike optical sensors where operating conditions arelimited by atmospheric conditions. A SAR image captures both shape andscattering information of ground objects, thereby providing goodrepresentation of the objects to develop SAR-ATR systems.

In recent years, maritime traffic has increased tremendously due to highdemand of global trade and sea food products. As shipping traffic grows,not only there is more likelihood of accidents and environmental damagebut also illegal maritime activities such as illegal fishing andtrafficking are increasing. Therefore, there is high requirement todevelop an efficient and reliable maritime surveillance system tomonitor and manage ocean activities around the globe. One of thesolutions for maritime surveillance is to develop a ship classificationor recognition system for recognizing shipping vessels and controllingsuspicious activities in ocean. For this purpose, SAR technology ishighly useful.

In literature, different approaches are developed to classify shipsusing SAR images. A general pipeline of conventional ship recognitionalgorithms is as follows. Firstly, a ship detection algorithm is appliedto detect ship and non-ship targets in SAR image and small sub-imageseach containing a ship are extracted from the full SAR image. Thesesmall sub-images are called ship chips. Secondly, these ship chips arepre-processed to remove the effect of side-lobes and background noise.Thirdly, features are extracted from the pre-processed ship chips todescribe various superstructures of ships. A superstructure is a part ofthe ship above the main deck, which consists of the forecastle, bridgeand various facilities that dominates the backscattering in SAR images.For example, in an oil tanker, the oil pipeline is a superstructure andforms a strong bright line at the center. Thus, the location of centerline of ship can be used as a feature to describe oil tanker. Finally,certain classification rules are defined on the extracted features tolabel the ship chips. Thus, ship superstructures are the most importantcomponents used for ship classification and to develop an accurate andefficient ship classification algorithm, accurate modelling ofsuperstructures is extremely important.

A prior art for ship classification using SAR images based on shipsuperstructures is disclosed in NPL 1. The technology disclosed in NPL 1uses three features to describe ship and its superstructures which are:Length (L), Stern-length-to-ship-length ratio (P), Ratio of Dimensions(ROD). ROD is a new feature proposed for describing superstructure inNPL 1. ROD is computed by taking the ratio of mean and standarddeviation of pixel backscattering values of bow and middle part of ship.

The technology disclosed in NPL 1 can provide good classification onlywhen the extracted shape and backscattering features are highlydiscriminative and are not sensitive to changes in SAR geometry.

CITATION LIST Non Patent Literature

NPL 1: “Ship Classification Based on Superstructure Scattering Featuresin SAR Images”, IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 13, NO.5, MAY 2016

SUMMARY OF INVENTION Technical Problem

However, in real world scenario, appearance of ships change with SARgeometry. Therefore, there is a possibility that the appearance of shipsbelonging to different classes may look similar due to the influence ofSAR geometry. Therefore, the technology disclosed in NPL 1 is notsufficient to distinguish various ship types. Therefore, morediscriminative features of ship and its superstructures are required toimprove the classification accuracy.

The above-described problem is not limited to just ship type recognitionbut can also be found in general object recognition using SAR. Thepresent disclosure has been made to solve the above-described problemand an object thereof is to provide an image processing apparatus, animage processing method, and an image processing program capable ofappropriately distinguishing various objects.

Solution to Problem

In a first example aspect, an image processing apparatus comprising:

detector means for detecting objects in an input SAR image andgenerating object chips;

projection calculator means for calculating projection information ofeach object using SAR geometry;

feature learner means for learning, for each object, a relation betweenan object chip and its projection information, and thereby generatinglearnt features of object chips; and

classifier means for classifying object chips into classes based on thelearnt features of object chips.

In a second example aspect, an image processing method comprising:

detecting objects in an input SAR image and generating object chips;

calculating projection information of each object using SAR geometry;

learning, for each object, a relation between an object chip and itsprojection information, and thereby generating learnt features of objectchips; and

classifying object chips into classes based on the learnt features ofobject chips.

In a third example aspect, a non-transitory computer readable mediumstoring an image processing program is a non-transitory computerreadable medium storing an image processing program for causing acomputer to execute an image processing method, the image processingmethod comprising:

detecting objects in an input SAR image and generating object chips;

calculating projection information of each object using SAR geometry;

learning, for each object, a relation between an object chip and itsprojection information, and thereby generating learnt features of objectchips; and

classifying object chips into classes based on the learnt features ofobject chips.

Advantageous Effects of Invention

According to the present disclosure, it is possible to provide an imageprocessing apparatus, an image processing method, and an imageprocessing program capable of appropriately distinguishing variousobjects.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows an example of an effect of a viewing direction of a radarsensor;

FIG. 2 shows an example of an SAR image geometry;

FIG. 3 is a block diagram showing a configuration example of an imageprocessing apparatus according to a first embodiment;

FIG. 4 shows an example of an effect of an SAR incident angle onprojection of a ship;

FIG. 5 is a diagram for explaining near-range and far-range incidentangles with respect to an SAR satellite position;

FIG. 6 shows an example of a foreshortening direction angle extractionfrom SAR GRD metadata;

FIG. 7 is a flowchart showing an example of an operation performed bythe image processing apparatus according to the first embodiment;

FIG. 8 is a block diagram showing a configuration example of an imageprocessing apparatus according to a second embodiment;

FIG. 9 is a flowchart showing an example of an operation performed bythe image processing apparatus according to the second embodiment in atraining mode;

FIG. 10 is a flowchart showing an example of an operation performed bythe image processing apparatus according to the second embodiment in anactual operational mode;

FIG. 11 is a block diagram showing a configuration example of an imageprocessing apparatus according to a third embodiment;

FIG. 12 is a flowchart showing an example of an operation performed bythe image processing apparatus according to the third embodiment in atraining mode;

FIG. 13 is a flowchart showing an example of an operation performed bythe image processing apparatus according to the third embodiment in anactual operational mode;

FIG. 14 is a block diagram showing a configuration example of an imageprocessing apparatus according to a fourth embodiment; and

FIG. 15 is a flowchart showing an example of an operation performed bythe image processing apparatus according to the fourth embodiment.

DESCRIPTION OF EMBODIMENTS

Embodiments according to the present disclosure are explained in detailwith reference to the drawings. The same components are denoted by thesame symbols throughout the drawings, and duplicated explanation isomitted as necessary for clarifying the explanation.

Prior to explaining embodiments, an SAR geometry is explainedhereinafter with reference to FIGS. 1 and 2. FIG. 1 illustrates theeffect of SAR geometry on appearance of an exemplary object. In thefigure, the same object is viewed from two different directions, whichin real world scenario can be considered as an example of two differentSAR geometries. The points A, B C and D are taken as example points onthe object. When the object is viewed from direction 1, the projectionof the points are A1, B1, C1 while D does not have any projection as itlies in the radar shadow and therefore, it is not sensed. Similarly,when viewed from direction 2, the projection of the points are B2, C2and D2 while A lies in the radar shadow. The order and the number ofprojections of the four points are not consistent when viewing the sameobject from different directions. Thus, it can be concluded that viewinggeometry of a radar sensor can affect the appearance of an object it issensing.

FIG. 2 shows an example of the SAR imaging geometry for a ship. The twoangles which are important to understand imaging geometry of the shipare incident angle (θ) and azimuth angle (φ). The incident angle (θ) isdefined as the angle between the radar line-of sight and the localvertical at the point where the radar intersects the earth or seasurface. The azimuth angle (φ) is defined as the angle between the shipheading direction and the range direction of radar wave on ground. Theappearance of the ship in the SAR image changes with changes in viewinggeometry as illustrated with an exemplary object in FIG. 1. Thus, SARgeometry plays a significant role in understanding electromagneticreflection from various ship superstructures.

The above explanation is with respect to single look complex (SLC)coordinate system. When the ship image is transformed from an SLC to ageocoded image, then the SLC coordinate system of the ship changes toearth geographic coordinate system. FIG. 2 also shows the earthgeographic coordinate system in 2D (2-dimension) using two orthogonalaxis (east-west and north-south). To understand the ship position inearth geographic coordinate system with respect to SAR satelliteposition, two more angles are defined as follows. The first angle is anorientation angle (φ′) of ship which is defined as the angle between theeast direction and the ship heading direction in earth geographiccoordinate system. The second angle is the angle between the eastdirection and the range direction, measured with respect to the SLCcoordinate system, towards the radar platform. This angle indicates theviewing direction of the satellite with respect to the ship position andtherefore, the direction towards the satellite where all the ship pointswill be projected. Thus, in this patent, this angle is defined asforeshortening direction angle.

First Embodiment

Next, a configuration example of an image processing apparatus 1according to a first embodiment is explained with reference to a blockdiagram shown in FIG. 3. The image processing apparatus 1 according tothe first embodiment includes a detector unit 11, a projectioncalculator unit 12, a normalizer unit 13, a feature learner unit 14, anda classifier unit 15.

An SAR GRD (Ground Range Detected) image is input to the detector unit11. The SAR GRD image is a geocoded image containing geographicalinformation of all the pixels in the image. The detector unit 11 detectsship targets present in the SAR GRD image. In particular, the detectorunit 11 applies ship detection algorithms to the SAR GRD image andproduces two outputs.

A first output of the two outputs generated by the detector unit 11 is aset of ship images each of which includes a ship at its center.Hereinafter, these ship images are also called “ship chips”. Note that,the “ship chips” are also called “ship patches”. A second output of thetwo outputs is geographical coordinates of a pixel located at the centerof each ship. Ships can be detected by using ship detection algorithmswell-developed in technical literatures, such as a Constant False AlarmRate (CFAR) and an Adaptive CFAR.

The detector unit 11 outputs each ship chip to the projection calculatorunit 12 and the normalizer unit 13. Further, the detector unit 11outputs the geographical coordinates of the central pixel of each shipto the projection calculator unit 12.

The projection calculator unit 12 is a unit that calculates projectioninformation of each ship using SAR geometry. Note that the projectioncalculator unit 12 calculates 3D geometrical information as theprojection information of each ship. Note that the projection calculatorunit 12 may calculate relationship between a radar transmitter and eachship as the 3D geometrical information. Note that the radar transmitteris a transmitter which transmits the SAR signal.

Further, the 3D geometrical information on the relationship between theradar transmitter and each ship is, for example, information on anincident angle (θ) of each ship. The incident angle (θ) is important SARgeometry information that can be used to extract 3D features(3-dimensional features) of the ship from a superstructure thereof.

Here, with reference to FIG. 4, it is explained that the projections ofship superstructures depend on the incident angle (θ). In FIG. 4, anexemplary ship target which is imaged by a SAR sensor under twodifferent incident angles (θ₁ and θ₂) is shown. In FIG. 4, in order toillustrate the influence of different incident angles, the ship targethas an exemplary superstructure BA on the ship.

In the incident angle θ₁, projection of BA is given by b_(s) and a_(s)in a slant range 1 and b_(g) and a_(g) in a ground range. As theincident angle changes from θ₁ to θ₂, projection of the samesuperstructure BA changes from b_(s) and a_(s) in the slant range 1 tob′_(s) and a′_(s) in a slant range 2, and from b_(g) and a_(g) of theground range to b′_(g) and a′_(g) thereof. It can be understood that theprojection makes the position B closer to the SAR sensor than theposition A even though the positions A and B are actually at the samedistance on the ground from the SAR sensor. Thus, the projection of aship superstructure changes when SAR incident angle changes. The smallerthe incident angle, the shorter is the projection length of thesuperstructure. In other words, the projection facing the radar sensoris foreshortened. Note that the direction of the foreshortenedprojection is a direction towards the radar platform.

Thus, the same superstructure BA shows different foreshortenedprojections at different incident angles. Hence, incident angle can beused as partial 3D information to understand 3D features of shipsuperstructures. The length of the foreshortened projection is calledforeshortening length.

The foreshortening length P of the superstructure BA located at a heighth at the incident angle θ can be defined as the below-shown Expression 1by using FIG. 4.

The projection calculator unit 12 is further explained hereinafter. SARGRD metadata is input to the projection calculator unit 12. Further, theprojection calculator unit 12 calculates an incident angle and aforeshortening length corresponding to the central pixel position ofeach ship detected by the detector unit 11. The SAR GRD metadata includenear-range and far-range incident angles information. The projectioncalculator unit 12 calculates the incident angle of the central pixel ofthe ship by using the near-range and far-range incident anglesinformation. Note that the SAR GRD metadata may include informationabout an SAR image resolution, a direction of geographic north, ageographic coordinate system of the geocoded image, a spatial extent ofthe imaged area, and a polarization and radiometric calibrationinformation in addition to the near-range and far-range incident anglesinformation.

An example of near-range and far-range incident angles for an SARsatellite (radar platform) position is explained hereinafter withreference to FIG. 5. Near-range and far-range are the extreme ranges ofthe SAR GRD image. The incident values for all pixels of the SAR GRDimage in between the near-range and the far-range are computed bynon-linear interpolation between near-range incident angle and far-rangeincident angle. These incident angles along with the geographicalcoordinates of the corresponding pixels are stored in a table.

The projection calculator unit 12 receives geographical coordinates ofthe central pixel of each ship chip from the detector unit 11. Further,the projection calculator unit 12 can acquire an incident anglecorresponding to the geographical coordinates of the central pixel ofeach ship chip by referring to a table.

Central pixel is used only as an exemplary point and a pixel other thanthe central pixel can also be used for projection calculation. Theprojection calculator unit 12 can calculate projection of the centralpixel of each ship by using the above-shown Expression 1. Further, thecalculated projection of the central pixel of each ship is the output ofthe projection calculator unit 12.

However, since height information “h” of the central pixel is unknown,the projection calculator unit 12 expresses the projection informationby using the incident angle θ alone. Alternatively, cot(θ) may be theoutput of the projection calculator unit 12.

As explained above with reference to FIG. 2, the foreshorteningdirection angle σ is an angle between east direction and range directiontowards the radar platform. Since foreshortening direction angle (σ) isimportant to understand direction of the projections, it may be alsooutput by the projection calculator unit 12.

To understand the calculation of foreshortening direction angle, FIG. 6is given. It shows a SAR GRD image with geographic north obtained fromthe SAR GRD metadata, range and azimuth directions. The geographic eastdirection can be derived based on the geographic north. Theforeshortening direction angle σ is given by the angle (in degrees)towards the radar platform between the range direction of the SAR GRDimage and the geographic east direction. FIG. 6 also shows an exemplaryship in the SAR GRD image in order to relate with the imaging geometryof ship in FIG. 2.

That is, the projection calculator unit 12 may calculate relationshipbetween a radar receiver and each ship as the 3D geometricalinformation. Note that the radar receiver is a receiver which receivesthe SAR signal. Further, the 3D geometrical information as therelationship between the radar receiver and each ship is, for example,information on a foreshortening direction angle (σ) or incident angle ofeach ship.

Further, the projection calculator unit 12 may calculate both theincident angle (θ) and the foreshortening direction angle (σ) of eachship. Hereinafter, an example in which the projection calculator unit 12calculates both the incident angle (θ) and the foreshortening directionangle (σ) of each ship is described.

The explanation is continued with reference to FIG. 3. The final outputof the projection calculator unit 12 is a 2D vector [θ,σ] for thecentral pixel of each ship. That is, the projection calculator unit 12outputs the 2D vector [θ,σ] for the central pixel of each ship to thefeature learner unit 14.

Since all inputs of the feature learner unit 14 are in the range between0 and 1, other forms which can be used as outputs of the projectioncalculator unit 12 are a 2D vector [cot(θ)cos(σ), cot(θ)sin(σ)], a 3Dvector of [cot(θ), cos(σ), sin(σ)] or other forms replacing cot( )function with cos( ) function.

The normalizer unit 13 receives the ship chip from the detector unit 11.The normalizer unit 13 normalizes each ship chip's pixel values by theirmean to make all the pixel values in the range between 0 and 1. Then,the normalizer unit 13 outputs each normalized ship chip to the featurelearner unit 14. Note that in the present embodiment, normalizer unit isan optional unit and the detected ship chips can be directly input tothe feature learner means 14 if the pixel values are pre-normalized inthe range 0 to 1.

Note that the normalizer unit 13 may have a function of performingpre-processing such as a removal of side-lobes, an extraction of aminimum enclosing rectangle, etc. in addition to the normalizingprocess. Alternatively, the image processing apparatus 1 may include aunit that performs pre-processing separately form the normalizer unit13, or may not have the function of performing pre-processing.

The feature learner unit 14 receives the projection information in theform of a 2D vector [θ,σ] for the central pixel of each ship from theprojection calculator unit 12. Further, the feature learner unit 14receives each normalized ship chip from the normalizer unit 13.

The feature learner unit 14 learns, for each ship, a relation betweenthe normalized ship chip and its projection information, and therebygenerates learnt features of ship chips. In particular, the featurelearner unit 14 learns 2D feature of ships such as areas and shapes aswell as learns the relationship between the ship chips and theirprojections [θ,σ] to obtain knowledge about 3D features ofsuperstructures of the ships. Note that every ship chip can be useful asan example of a learning process. The feature learner unit 14 may be anauto-encoder capable of learning features of ships.

The learnt features of ship chips can be expressed by a multidimensionalvector Z which is a combination of 2D features of the ships and 3Dfeatures thereof. The vector Z is a vector of numbers representinglearnt features of the input ships by the feature learner unit 14. Thefeature learner unit 14 creates non-linear mappings between pixels ofthe ship chips and their corresponding projection information throughmachine learning. The number in the vector Z represent an output of thenon-linear mappings.

Then, the feature learner unit 14 outputs learnt features of ship chipsto the classifier unit 15.

The classifier unit 15 receives the learnt features of ship chips fromthe feature learner unit 14. Further, the classifier unit 15 classifieseach ship chip into one of classes based on the learnt features of shipchips. Note that for the classification of ship chips into classes,well-known classification algorithms such as Support Vector Machines andNeural Networks can be used.

Next, an example of an operation performed by the image processingapparatus 1 according to the first embodiment is explained withreference to a flowchart shown in FIG. 7.

Firstly, the image processing apparatus 1 detects ships in an input SARimage and generates ship chips thereof by using the detector unit 11(step S101).

Next, the image processing apparatus 1 calculates projection informationof each ship by using the projection calculator unit 12 (step S102).Note that the projection information of each ship includes informationabout an incident angle (θ) or foreshortening direction angle (σ) ofeach ship.

Next, the image processing apparatus 1 normalizes pixel values of eachship chip by using the normalizer unit 13 (step S103).

Next, the image processing apparatus 1 learns, for each ship, a relationbetween the normalized ship chip and its projection information, andthereby generates learnt features of ship chips by using the featurelearner unit 14 (step S104).

Next, the image processing apparatus 1 classifies each ship chip intoone of classes based on the learnt features of ship chips by using theclassifier unit 15 (step S105).

Note that although an example in which the process for calculatingprojection information of each ship in the step S102 is performed beforethe process for normalizing pixel values of each ship chip in the stepS103 is shown in FIG. 7, the order of processes is not limited to thisexample. The process in the step S103 may be performed before theprocess in the step S102.

As described above, the image processing apparatus 1 according to thefirst embodiment of the present disclosure is configured so that theprojection calculator unit 12 calculates 3D geometrical information asthe projection information of each ship. Note that the projectioncalculator unit 12 may calculate at least one of relationship between aradar transmitter and each ship and relationship between a radarreceiver and each ship as the 3D geometrical information. Further, the3D geometrical information on the relationship between the radartransmitter and each ship may be information on an incident angle (θ) ofeach ship. Further, the 3D geometrical information on the relationshipbetween the radar receiver and each ship may be information on aforeshortening direction angle (σ) of each ship. Note that the incidentangle (θ) and the foreshortening direction angle (σ) are useful forrecognizing a size of a superstructure and a position thereof, which arekey information for ship recognition. That is, the incident angle (θ)and the foreshortening direction angle (σ) can be used as partial 3Dinformation to understand 3D features of ship superstructures.Therefore, in the image processing apparatus 1, partial 3D informationcan be extracted from an SAR geometry in a form of projection.

Further, the image processing apparatus 1 is configured so that thefeature learner unit 14 learns, for each ship, a relation between anormalized ship chip and its projection information, and therebygenerates learnt features of ship chips. In this way, in the imageprocessing apparatus 1, it is possible to learn a relation betweenprojection information (i.e., partial 3D information) and itscorresponding ship image, and thereby to extract 3D structuralinformation about the superstructure of the ship.

Further, the image processing apparatus 1 is configured so that theclassifier unit 15 classifies each ship chip into one of classes basedon the learnt features of ship chips. As a result, the image processingapparatus 1 can improve the accuracy of the classification of ships.That is, the image processing apparatus 1 according to the firstembodiment can provide an image processing apparatus capable ofappropriately distinguishing various ship types.

Further, the feature learner unit 14 has another advantageous effectthat it can automatically learn 2D features of ships.

Second Embodiment

Next, a configuration example of an image processing apparatus 1Aaccording to a second embodiment of the present disclosure is explainedwith reference to a block diagram shown in FIG. 8. The image processingapparatus 1A according to the second embodiment includes a detector unit11, a projection calculator unit 12, a normalizer unit 13, a featurelearner unit 14A, a classifier unit 15A, a cost calculator unit 16, aparameter updater unit 17, and a storage unit 18. Note thatconfigurations of the detector unit 11, the projection calculator unit12, and the normalizer unit 13 of the image processing apparatus 1A aresimilar to those of the image processing apparatus 1 according to thefirst embodiment, and therefore their explanations are omitted.

The feature learner unit 14A and the classifier unit 15A operate in twomodes, i.e., a training mode and an actual operational mode. Further,the cost calculator unit 16 and the parameter updater unit 17 operate inthe training mode.

Firstly, the training mode is explained. In the training mode, thefeature learner unit 14A receives a 2D vector [θ,σ] for each trainingship from the projection calculator unit 12. Note that the training shipmeans a target ship in the training mode. Further, the feature learnerunit 14A receives a plurality of normalized training ship chips from thenormalizer unit 13. Further, the feature learner unit 14A learns, foreach training ship, a relation between a normalized training ship chipand a 2D vector [θ,σ] of the training ship, and thereby generates learntfeatures (Z_train) of training ship chips. Then, the feature learnerunit 14A outputs the learnt features (Z_train) of training ship chips tothe classifier unit 15A.

In the training mode, the classifier unit 15A receives the learntfeatures (Z_train) of training ship chips from the feature learner unit14A. Further, the classifier unit 15A estimates classes of training shipchips based on the learnt features (Z_train) of training ship chips.Then, the classifier unit 15A outputs the estimated classes of trainingship chips to the cost calculator unit 16.

The cost calculator unit 16 receives the estimated classes of trainingship chips from the classifier unit 15A. Further, actual classes fortraining ship chips are input to the cost calculator unit 16. Further,the cost calculator unit 16 calculates a cost between the estimatedclasses of training ship chips and the actual classes of that trainingship chips as a misclassification error therebetween. Then, the costcalculator unit 16 outputs the calculated cost to the parameter updaterunit 17.

The parameter updater unit 17 receives the cost from the cost calculatorunit 16. Further, the parameter updater unit 17 updates feature leanerparameters of the feature learner unit 14A and classifier parameters ofthe classifier unit 15A so that the cost is minimized. Note that theminimization of the cost can be performed by using an ordinaryoptimization algorithm such as gradient descent. The minimization of thecost is continued (or repeated) until the cost converges to a state inwhich the cost function cannot be reduced any longer. Note that updatedfeature leaner parameters that are obtained after the minimization ofthe cost are also called “trained feature leaner parameters”. Further,updated classifier parameters that are obtained after the minimizationof the cost are also called “trained classifier parameters”. Afterperforming the minimization of the cost, the parameter updater unit 17stores the trained feature leaner parameters and the trained classifierparameters into the storage unit 18.

Next, the actual operational mode is explained. In the actualoperational mode, the feature learner unit 14A receives a 2D vector[θ,σ] for each newly-detected ship chip from the projection calculatorunit 12. Further, the feature learner unit 14A receives a normalizedship chip for each newly-detected ship from the normalizer unit 13.Further, the feature learner unit 14A reads trained feature leanerparameters from the storage unit 18. Further, the feature learner unit14A uses each normalized ship chip and its 2D vector [θ,σ] as inputvalues and generates learnt features (Z) of each newly-detected shipchip by using the trained feature leaner parameters. Then the featurelearner unit 14A outputs the learnt features (Z) of each newly-detectedship chip to the classifier unit 15A.

In the actual operational mode, the classifier unit 15A receives thelearnt features (Z) of each newly-detected ship chip from the featurelearner unit 14A. Further, the classifier unit 15A reads trainedclassifier parameters from the storage unit 18. Further, the classifierunit 15A uses learnt features (Z) of each ship as input values andclassifies each newly-detected ship chip into one of classes by usingthe trained classifier parameters. Then the classifier unit 15A outputsthe classes into which the newly-detected ship chips have beenclassified. Note that the classes output from the classifier unit 15Aare classes of ships present in the SAR GRD image input to the detectorunit 11 in the actual operational mode.

Next, an example of an operation performed by the image processingapparatus 1A according to the second embodiment in the training mode isexplained with reference to a flowchart shown in FIG. 9.

Firstly, in the image processing apparatus 1A, a SAR GRD image fortraining ships is input to the detector unit 11 (step S201).

Next, the image processing apparatus 1A detects training ships in theinput SAR GRD image and generates training ship chips thereof by usingthe detector unit 11 (step S202).

Next, the image processing apparatus 1A normalizes pixel values of eachtraining ship chip by using the normalizer unit 13 (step S203).

In parallel to the processes in the steps S201 to S203, processes insteps S204 and S205 are performed. Note that the process in the stepS205 is a process that is performed by using training ship chipsgenerated in the step S202 and hence performed after the step S202.

SAR GRD metadata for each training ship is input to the projectioncalculator unit 12 (step S204). Next, the image processing apparatus 1Acalculates, by using the projection calculator unit 12, incident angle(θ) and foreshortening direction angle (σ) for each training ship byusing the SAR GRD metadata (step S205).

After the steps S203 and S205, by using the feature learner unit 14A,the image processing apparatus 1A learns, for each training ship, arelation between a normalized training ship chip and a 2D vector [θ,σ]of the training ship, and thereby generates learnt features (Z_train) oftraining ship chips (step S206).

Next, the image processing apparatus 1A estimates classes of trainingship chips based on the learnt features (Z_train) of training ship chipsby using the classifier unit 15A (step S207).

Next, the image processing apparatus 1A calculates a cost between theestimated classes of training ship chips and the actual classes of thetraining ship chips as a misclassification error therebetween by usingthe cost calculator unit 16 (step S208).

Next, the image processing apparatus 1A updates, by using the parameterupdater unit 17, feature leaner parameters of the feature learner unit14A and classifier parameters of the classifier unit 15A so that thecost is minimized (step S209).

Next, the image processing apparatus 1A determines whether or not thecost has converged by using the parameter updater unit 17 (step S210).

When the image processing apparatus 1A determines that the cost has notconverged yet (NO at step S210), the image processing apparatus 1Areturns to the step S206. Then, the image processing apparatus 1Aperforms the processes in the steps S206 to S210 again. On the otherhand, when the image processing apparatus 1A determines that the costhas converged (YES at step S210), the image processing apparatus 1Astores the trained feature leaner parameters and the trained classifierparameters into the storage unit 18 (step S211).

Next, an example of an operation performed by the image processingapparatus 1A according to the second embodiment in the actualoperational mode is explained with reference to a flowchart shown inFIG. 10.

Firstly, in the image processing apparatus 1A, a SAR GRD image for eachnewly-detected ship is input to the detector unit 11 (step S301).

Next, the image processing apparatus 1A detects each ship in the inputSAR GRD image and generates each ship chip by using the detector unit 11(step S302).

Next, the image processing apparatus 1A normalizes pixel values of eachship chip by using the normalizer unit 13 (step S303).

In parallel to the processes in the steps S301 to S303, processes insteps S304 and S305 are performed. Note that the process in the stepS305 is a process that is performed by using each ship chip generated inthe step S302 and hence performed after the step S302.

SAR GRD metadata for each newly-detected ship is input to the projectioncalculator unit 12 (step S304). Next, the image processing apparatus 1Acalculates, by using the projection calculator unit 12, incident angle(θ) and foreshortening direction angle (σ) for each ship by using theSAR GRD metadata (step S305).

After the steps S303 and S305, by using the feature learner unit 14A,the image processing apparatus 1A uses each normalized ship chip and its2D vector [θ,σ] as input values and generates learnt features (Z) ofeach ship by using trained feature leaner parameters (step S306).

Next, by using the classifier unit 15A, the image processing apparatus1A uses the learnt features (Z) of each ship as input values andclassifies each ship chip into one of classes by using trainedclassifier parameters (step S307).

As described above, the image processing apparatus 1A according to thesecond embodiment is configured so that the feature learner unit 14Alearns, for each training ship, a relation between a normalized trainingship chip and a 2D vector [θ,σ] of the training ship, and therebygenerates learnt features (Z_train) of training ship chips in thetraining mode. Further, the image processing apparatus 1A is configuredso that the classifier unit 15A estimates classes of training ship chipsbased on the learnt features (Z_train) of training ship chips in thetraining mode. Further, the image processing apparatus 1A is configuredso that the cost calculator unit 16 calculates a cost between theestimated classes of training ship chips and the actual classes oftraining ship chips as a misclassification error therebetween. Further,the image processing apparatus 1A is configured so that the parameterupdater unit 17 updates feature leaner parameters and classifierparameters so that the cost is minimized. In this way, in the imageprocessing apparatus 1A, each normalized ship chip and a 2D vector [θ,σ]of the ship are used as input value, and parameters in the featurelearner unit 14A and the classifier unit 15A can be trained (i.e.,improved) so that the misclassification error, which occurs when eachnewly-detected ship chip is classified into one of classes, isminimized.

Third Embodiment

Next, a configuration example of an image processing apparatus 1Baccording to a third embodiment of the present disclosure is explainedwith reference to a block diagram shown in FIG. 11. The image processingapparatus 1B according to the third embodiment includes a detector unit11, a projection calculator unit 12, a feature learner unit 14A, aclassifier unit 15A, a cost calculator unit 16, a parameter updater unit17, a storage unit 18, a rotating normalizer unit 19, and a projectionrotator unit 20. That is, compared to the image processing apparatus 1Aaccording to the second embodiment, the image processing apparatus 1Baccording to the third embodiment includes the rotating normalizer unit19 in place of the normalizer unit 13 and further includes theprojection rotator unit 20. Note that configuration of the imageprocessing apparatus 1B is similar to that of the image processingapparatus 1A according to the second embodiment except for theconfiguration of the rotating normalizer unit 19 and the projectionrotator unit 20, and therefore the explanation of the similar part isomitted.

In the above explanation, an incident angle (θ) from a SAR satellite isexplained as a factor that affects the appearance of an object in an SARgeometry in the earth geographic coordinate system. Another factor thataffects the appearance of an object in the SAR geometry in the earthgeographic coordinate system is an orientation angle φ′. For example,when a ship shown in FIG. 2 is tuned by an example orientation angle,e.g., by (φ′+180) degrees, the stern of the ship is detected before thebow thereof is detected. Therefore, the ship appears in the SAR image ina different appearance (i.e., in a different shape). This factor couldapply to other ship components such as a superstructure thereof.Therefore, both of the incident angle θ and orientation angle φ′ affectthe projection of the 3D superstructure of the ship in the SAR geometry.That is, a change in the orientation angle φ′ changes the projection ofthe ship in the SAR image.

Rotating normalizer unit 19 normalizes each ship chip's pixel values bytheir mean to make all the pixel values in the range between 0 and 1.The said function is optional if the pixel values of the ship chips arepre-normalized in the range between 0 to 1. Further, the rotatingnormalizer unit 19 determines the orientation angle φ′ of each shipchip. Note that for the determination of the orientation angle φ′ ofeach ship chip, algorithms such as a Principal Component Analysis and aRadon Transform may be used. Further, the rotating normalizer unit 19rotates each normalized ship chip by the determined orientation angle φ′and thereby aligns all the normalized ship chips so that they point inthe east direction. Note that the east is shown in FIG. 2. Then, therotating normalizer unit 19 outputs information of each of thenormalized ship chips pointed in the east direction to the featurelearner unit 14A. Further, the rotating normalizer unit 19 outputs theorientation angle φ′ of each ship chip to the projection rotator unit20.

As described above, all the normalized ship chips are aligned by therotating normalizer unit 19 so that they point in the east direction. Asa result of rotation of each ship, their corresponding foreshorteningdirection angles will also change. The projection rotator unit 20performs a process in which an effect of the orientation of each ship onits projection is taken into account.

The projection rotator unit 20 receives a 2D vector [θ,σ] for thecentral pixel of each ship from the projection calculator unit 12.Further, the projection rotator unit 20 receives the orientation angleφ′ of each ship chip from the rotating normalizer unit 19. Further, theprojection rotator unit 20 generates a new 2D vector [θ,σ′] of each shipby rotating the 2D vector [θ,σ] for the central pixel of that ship bythe corresponding orientation angle φ′ of that ship. Note that theforeshortening direction angle σ′ is an angle obtained by subtractingthe orientation angle φ′ from the foreshortening direction angle σ.Further, the projection rotator unit 20 outputs the new 2D vector [θ,σ′]of each ship to the feature learner unit 14A.

Next, an example of an operation performed by the image processingapparatus 1B according to the third embodiment in the training mode isexplained with reference to a flowchart shown in FIG. 12. Note thatsteps S401, S402, S405, S406, and S409-S413 in FIG. 12 are similar tothe steps S201, S202, S204, S205 and S207-S211 in FIG. 9, and thereforetheir explanations are omitted. Note that the steps S405 to S407 areperformed in parallel with the steps S401 to S404. However, the stepS406 is performed by using training ship chips generated in the stepS402 and hence performed after the step S402. Further, the step S407 isperformed by using the orientation angle φ′ of each training ship chipdetermined in the step S403 and hence performed after the step S403.

By using the rotating normalizer unit 19, the image processing apparatus1B normalizes each training ship chip's pixel values and determines theorientation angle φ′ of each training ship chip (step S403).

Next, the image processing apparatus 1B rotates, by using the rotatingnormalizer unit 19, each normalized training ship chip by thecorresponding orientation angle φ′ (step S404).

By using the projection rotator unit 20, the image processing apparatus1B rotates a 2D vector [θ,σ] of each training ship by the correspondingorientation angle φ′ of the training ship chip and thereby generates anew 2D vector [θ,σ′] of each training ship (step S407).

After the steps S404 and S407, by using the feature learner unit 14A,the image processing apparatus 1B learns, for each training ship, arelation between a normalized and rotated training ship chip and a 2Dvector [θ,σ′] of the training ship, and thereby generates learntfeatures (Z_train) of training ship chips (step S408).

Next, an example of an operation performed by the image processingapparatus 1B according to the third embodiment in the actual operationalmode is explained with reference to a flowchart shown in FIG. 13. Notethat steps S501, S502, S505, S506, and S509 in FIG. 13 are similar tothe steps S301, S302, S304, S305 and S307 in FIG. 10, and thereforetheir explanations are omitted. Note that the steps S505 to S507 areperformed in parallel with the steps S501 to S504. However, the stepS506 is performed by using each ship chip generated in the step S502 andhence performed after the step S502. Further, the step S507 is performedby using the orientation angle φ′ of each ship chip determined in thestep S503 and hence performed after the step S503.

By using the rotating normalizer unit 19, the image processing apparatus1B normalizes each ship chip's pixel values and determines theorientation angle φ′ of each ship chip (step S503).

Next, the image processing apparatus 1B rotates, by using the rotatingnormalizer unit 19, each normalized ship chip by the correspondingorientation angle φ′ (step S504).

By using the projection rotator unit 20, the image processing apparatus1B rotates a 2D vector [θ,σ] of each ship by the correspondingorientation angle φ′ of the ship chip and thereby generates a new 2Dvector [θ,σ′] of each ship (step S507).

After the steps S504 and S507, by using the feature learner unit 14A,the image processing apparatus 1B uses each normalized ship chip and its2D vector [θ,σ′] as input values and generates learnt features (Z) ofeach ship by using trained feature leaner parameters (step S508).

As described above, the image processing apparatus 1B according to thethird embodiment of the present disclosure is configured to, by usingthe rotating normalizer unit 19, normalize each ship chip's pixel valuesand determine the orientation angle φ′ of each ship chip. Further, theimage processing apparatus 1B is configured to, by using the rotatingnormalizer unit 19, rotates each normalized ship chip by thecorresponding orientation angle φ′. In this way, the image processingapparatus 1B can align all the ship chips so that they point in the eastdirection.

Further, the image processing apparatus 1B is configured to, by usingthe projection rotator unit 20, rotates a 2D vector [θ,σ] of the centralpixel of each ship by the corresponding orientation angle φ′ of the shipchip and thereby generates a new 2D vector [θ, σ′] of each ship. In thisway, the image processing apparatus 1B can generate a 2D vector [θ, σ′]of each ship while taking not only the incident angle θ from the SARsatellite but also the orientation angle φ′ into account as factors thataffect the appearance of an object in the SAR geometry in the earthgeographic coordinate system. Therefore, compared to the imageprocessing apparatus 1A according to the second embodiment, the imageprocessing apparatus 1B according to the third embodiment can take moreinformation that affect the superstructure of each ship intoconsideration. Consequently, it is possible to improve the accuracy ofthe learning process in the feature learner unit 14A. Accordingly, theimage processing apparatus 1B can improve the accuracy forclassification of ships in the SAR image.

Fourth Embodiment

Although the first embodiment to the third embodiment is described aboutship type recognition, the first embodiment to the third embodiment canalso be applied to general object recognition. In a fourth embodiment,general object recognition is described.

A configuration example of an image processing apparatus 1C according tothe fourth embodiment of the present disclosure is explained withreference to a block diagram shown in FIG. 14. The image processingapparatus 1C according to the fourth embodiment includes a detector unit11, a projection calculator unit 12, a feature learner unit 14, and aclassifier unit 15.

The detector unit 11 detects object targets present in the SAR GRDimage. Further, the detector unit 11 generates object chips. Further,the detector unit 11 outputs each object chip to the projectioncalculator unit 12 and the feature learner unit 14. Further, thedetector unit 11 outputs the geographical coordinates of the centralpixel of each object to the projection calculator unit 12.

The projection calculator unit 12 calculates projection information ofeach object using SAR geometry. Further, the projection calculator unit12 outputs the projection information of each object to the featurelearner unit 14.

The feature learner unit 14 learns for each object, a relation betweenan object chip and its projection information, and thereby generateslearnt features of object chips. Further, the feature learner unit 14outputs the learnt features of object chips to the classifier unit 15.

The classifier unit 15 classifies object chips into classes based on thelearnt features of object chips.

Next, an example of an operation performed by the image processingapparatus 1C according to the fourth embodiment is explained withreference to a flowchart shown in FIG. 15.

Firstly, the image processing apparatus 1C detects objects in an inputSAR image and generates object chips thereof by using the detector unit11 (step S601).

Next, the image processing apparatus 1C calculates projectioninformation of each object using SAR geometry by the projectioncalculator unit 12 (step S602).

Next, the image processing apparatus 1C learns, for each object, arelation between an object chip and its projection information, andthereby generates learnt features of object chips by using the featurelearner unit 14 (step S603).

Next, the image processing apparatus 1C classifies each object chip intoone of classes based on the learnt features of object chips by using theclassifier unit 15 (step S604).

As described above, the image processing apparatus 1C according to thefourth embodiment of the present disclosure is configured so that theprojection calculator unit 12 calculates projection information of eachobject using SAR geometry. Therefore, in the image processing apparatus1C, partial 3D information can be extracted from an SAR geometry in aform of projection.

Further, the image processing apparatus 1C is configured so that thefeature learner unit 14 learns, for each object, a relation between anobject chip and its projection information, and thereby generates learntfeatures of object chips. In this way, in the image processing apparatus1C, it is possible to learn a relation between projection information(i.e., partial 3D information) and its corresponding object image, andthereby to extract 3D structural information about the superstructure ofthe object.

Further, the image processing apparatus 1C is configured so that theclassifier unit 15 classifies each object chip into one of classes basedon the learnt features of object chips. As a result, the imageprocessing apparatus 1C can improve the accuracy of the classificationof objects. That is, the image processing apparatus 1C according to thefourth embodiment can provide an image processing apparatus capable ofappropriately distinguishing various objects.

Further, although the present disclosure is described as a hardwareconfiguration in the above-described embodiments, the present disclosureis not limited to the hardware configurations. The present disclosurecan be implemented by having a processor such as a CPU (CentralProcessing Unit) included in the image processing apparatus to execute acomputer program for performing each process in each of theabove-described functions.

In the above-described examples, the program can be stored in varioustypes of non-transitory computer readable media and thereby supplied tocomputers. The non-transitory computer readable media includes varioustypes of tangible storage media. Examples of the non-transitory computerreadable media include a magnetic recording medium (such as a flexibledisk, a magnetic tape, and a hard disk drive), a magneto-optic recordingmedium (such as a magneto-optic disk), a CD-ROM (Read Only Memory), aCD-R, and a CD-R/W, a DVD (Digital Versatile Disc), a BD (Blu-ray(registered trademark) Disc), and a semiconductor memory (such as a maskROM, a PROM (Programmable ROM), an EPROM (Erasable PROM), a flash ROM,and a RAM (Random Access Memory)). Further, the program can be suppliedto computers by using various types of transitory computer readablemedia. Examples of the transitory computer readable media include anelectrical signal, an optical signal, and an electromagnetic wave. Thetransitory computer readable media can be used to supply programs tocomputer through a wire communication path such as an electrical wireand an optical fiber, or wireless communication path.

Although the present disclosure is explained above with reference toembodiments, the present disclosure is not limited to theabove-described embodiments. Various modifications that can beunderstood by those skilled in the art can be made to the configurationand details of the present disclosure within the scope of the invention.

The whole or part of the embodiments disclosed above can be describedas, but not limited to, the following supplementary notes.

(Supplementary Note 1)

An image processing apparatus comprising:

detector means for detecting objects in an input SAR image andgenerating object chips;

projection calculator means for calculating projection information ofeach object using SAR geometry;

feature learner means for learning, for each object, a relation betweenan object chip and its projection information, and thereby generatinglearnt features of object chips; and

classifier means for classifying object chips into classes based on thelearnt features of object chips.

(Supplementary Note 2)

The image processing apparatus according to Supplementary note 1,wherein the projection calculator means calculates 3D geometricalinformation as the projection information of each object, wherein the 3Dgeometrical information relates to at least one of relationship betweena radar transmitter and each object and relationship between a radarreceiver and each object.

(Supplementary Note 3)

The image processing apparatus according to Supplementary note 1,wherein the projection calculator means calculates at least one of anincident angle of each object and a foreshortening direction angle ofeach object as the projection information of each object.

(Supplementary Note 4)

The image processing apparatus according to Supplementary note 1,wherein the projection calculator means calculates a 2D vector [θ,σ] asthe projection information of each object, wherein the θ is an incidentangle of that object and the σ is a foreshortening direction angle ofthat object.

(Supplementary Note 5)

The image processing apparatus according to Supplementary note 4,further comprising cost calculator means, parameter updater means, andstorage means, wherein

the feature learner means learns, for each training object, a relationbetween a training object chip and a 2D vector [θ,σ] of the trainingobject, and thereby generates learnt features (Z_train) of trainingobject chips in training mode,

the classifier means estimates classes of training object chips based onthe learnt features (Z_train) of training object chips in the trainingmode,

the cost calculator means calculates a cost between an estimated classesof training object chips and an actual classes of that training objectchips as a misclassification error therebetween in the training mode,

the parameter updater means updates feature leaner parameters of thefeature learner means and classifier parameters of the classifier meansso that the cost is minimized, and

the parameter updater means stores updated feature leaner parameters andupdated classifier parameters into the storage means.

(Supplementary Note 6)

The image processing apparatus according to Supplementary note 5,wherein

the feature learner means uses an object chip for each newly-detectedobject and its 2D vector [θ,σ] as input values and generates learntfeatures (Z) of each newly-detected object chip by using the updatedfeature leaner parameters in operational mode, and

the classifier means uses the learnt features (Z) of newly-detectedobject chip as input values and classifies each newly-detected objectchip into one of classes by using the updated classifier parameters inthe operational mode.

(Supplementary Note 7)

The image processing apparatus according to any one of Supplementarynotes 1 to 6, further comprising normalizer means for normalizing pixelvalues of each object chip.

(Supplementary Note 8)

The image processing apparatus according to any one of Supplementarynotes 4 to 6, further comprising projection rotator means and rotatingnormalizer means, wherein

the rotating normalizer means normalizes pixel values of each objectchip, determines an orientation angle φ′ of each object chip, androtates each normalized object chip by its determined orientation angleφ′, and

the projection rotator means rotates a 2D vector [θ,σ] of each object byits orientation angle φ′ and thereby generates a new 2D vector [θ,σ′] ofeach object, wherein the σ′ is an angle obtained by subtracting theorientation angle φ′ from the foreshortening direction angle σ.

(Supplementary Note 9)

The image processing apparatus according to any one of Supplementarynotes 4 to 6 and 8, wherein the projection calculator means calculates a2D or 3D vector containing other forms and combinations of the incidentangle and the foreshortening direction angle such as [cot(θ)cos(σ),cot(θ)sin(σ)] or [cot(θ), cos(σ), sin(σ)] as the projection informationof each object.

(Supplementary Note 10)

The image processing apparatus according to any one of Supplementarynotes 1 to 9, wherein the detector means is either a Constant FalseAlarm Rate (CFAR) or adaptive CFAR.

(Supplementary Note 11)

The image processing apparatus according to any one of Supplementarynotes 1 to 10, wherein the feature learner means is a type ofauto-encoder capable of learning features of objects.

(Supplementary Note 12)

The image processing apparatus according to any one of Supplementarynotes 1 to 11, wherein the classifier means is either a Support VectorMachine or a Neural Network.

(Supplementary Note 13)

An image processing method comprising:

detecting objects in an input SAR image and generating object chips;

calculating projection information of each object using SAR geometry;

learning, for each object, a relation between an object chip and itsprojection information, and thereby generating learnt features of objectchips; and

classifying object chips into classes based on the learnt features ofobject chips.

(Supplementary Note 14)

The image processing method according to Supplementary note 13, whereinthe projection information of each object is 3D geometrical information,wherein the 3D geometrical information relates to at least one ofrelationship between a radar transmitter and each object andrelationship between a radar receiver and each object.

(Supplementary Note 15)

A non-transitory computer readable medium storing an image processingprogram is a non-transitory computer readable medium storing an imageprocessing program for causing a computer to execute an image processingmethod, the image processing method comprising:

detecting objects in an input SAR image and generating object chips;

calculating projection information of each object using SAR geometry;

learning, for each object, a relation between an object chip and itsprojection information, and thereby generating learnt features of objectchips; and

classifying object chips into classes based on the learnt features ofobject chips.

(Supplementary Note 16)

The non-transitory computer readable medium storing the image processingprogram according to Supplementary note 15, wherein the projectioninformation of each object is 3D geometrical information, wherein the 3Dgeometrical information relates to at least one of relationship betweena radar transmitter and each object and relationship between a radarreceiver and each object.

REFERENCE SIGNS LIST

-   1, 1A, 1B, 1C Image processing apparatus-   11 detector unit-   12 projection calculator unit-   13 normalizer unit-   14, 14A feature learner unit-   15, 15A classifier unit-   16 cost calculator unit-   17 parameter updater unit-   18 storage unit-   19 rotating normalizer unit-   20 projection rotator unit

1. An image processing apparatus comprising: Detector unit configured todetect objects in an input SAR image and generating object chips;projection calculator unit configured to calculate projectioninformation of each object using SAR geometry; feature learner unitconfigured to learn, for each object, a relation between an object chipand its projection information, and thereby generating learnt featuresof object chips; and classifier unit configured to classify object chipsinto classes based on the learnt features of object chips.
 2. The imageprocessing apparatus according to claim 1, wherein the projectioncalculator unit calculates 3D geometrical information as the projectioninformation of each object, wherein the 3D geometrical informationrelates to at least one of relationship between a radar transmitter andeach object and relationship between a radar receiver and each object.3. The image processing apparatus according to claim 1, wherein theprojection calculator unit calculates at least one of an incident angleof each object and a foreshortening direction angle of each object asthe projection information of each object.
 4. The image processingapparatus according to claim 1, wherein the projection calculator unitcalculates a 2D vector [θ,σ] as the projection information of eachobject, wherein the θ is an incident angle of that object and the σ is aforeshortening direction angle of that object.
 5. The image processingapparatus according to claim 4, further comprising cost calculator unit,parameter updater unit, and storage unit, wherein the feature learnerunit learns, for each training object, a relation between a trainingobject chip and a 2D vector [θ,σ] of the training object, and therebygenerates learnt features (Z_train) of training object chips in trainingmode, the classifier unit estimates classes of training object chipsbased on the learnt features (Z_train) of training object chips in thetraining mode, the cost calculator unit calculates a cost between anestimated classes of training object chips and an actual classes of thattraining object chips as a misclassification error therebetween in thetraining mode, the parameter updater unit updates feature leanerparameters of the feature learner unit and classifier parameters of theclassifier unit so that the cost is minimized, and the parameter updaterunit stores updated feature leaner parameters and updated classifierparameters into the storage unit.
 6. The image processing apparatusaccording to claim 5, wherein the feature learner unit uses an objectchip for each newly-detected object and its 2D vector [θ,σ] as inputvalues and generates learnt features (Z) of each newly-detected objectchip by using the updated feature leaner parameters in operational mode,and the classifier unit uses the learnt features (Z) of newly-detectedobject chip as input values and classifies each newly-detected objectchip into one of classes by using the updated classifier parameters inthe operational mode.
 7. The image processing apparatus according toclaim 1, further comprising normalizer unit configured to normalizepixel values of each object chip.
 8. The image processing apparatusaccording to claim 4, further comprising projection rotator unit androtating normalizer unit, wherein the rotating normalizer unitnormalizes pixel values of each object chip, determines an orientationangle φ′ of each object chip, and rotates each normalized object chip byits determined orientation angle φ′, and the projection rotator unitrotates a 2D vector [θ,σ] of each object by its orientation angle φ′ andthereby generates a new 2D vector [θ,σ′] of each object, wherein the σ′is an angle obtained by subtracting the orientation angle φ′ from theforeshortening direction angle σ.
 9. The image processing apparatusaccording to claim 4, wherein the projection calculator unit calculatesa 2D or 3D vector containing other forms and combinations of theincident angle and the foreshortening direction angle such as[cot(θ)cos(σ), cot(θ)sin(σ)] or [cot(θ), cos(σ), sin(σ)] as theprojection information of each object.
 10. The image processingapparatus according to claim 1, wherein the detector unit is either aConstant False Alarm Rate (CFAR) or adaptive CFAR.
 11. The imageprocessing apparatus according to claim 1, wherein the feature learnerunit is a type of auto-encoder capable of learning features of objects.12. The image processing apparatus according to claim 1, wherein theclassifier unit is either a Support Vector Machine or a Neural Network.13. An image processing method comprising: detecting objects in an inputSAR image and generating object chips; calculating projectioninformation of each object using SAR geometry; learning, for eachobject, a relation between an object chip and its projectioninformation, and thereby generating learnt features of object chips; andclassifying object chips into classes based on the learnt features ofobject chips.
 14. The image processing method according to claim 13,wherein the projection information of each object is 3D geometricalinformation, wherein the 3D geometrical information relates to at leastone of relationship between a radar transmitter and each object andrelationship between a radar receiver and each object.
 15. Anon-transitory computer readable medium storing an image processingprogram is a non-transitory computer readable medium storing an imageprocessing program for causing a computer to execute an image processingmethod, the image processing method comprising: detecting objects in aninput SAR image and generating object chips; calculating projectioninformation of each object using SAR geometry; learning, for eachobject, a relation between an object chip and its projectioninformation, and thereby generating learnt features of object chips; andclassifying object chips into classes based on the learnt features ofobject chips.
 16. The non-transitory computer readable medium storingthe image processing program according to claim 15, wherein theprojection information of each object is 3D geometrical information,wherein the 3D geometrical information relates to at least one ofrelationship between a radar transmitter and each object andrelationship between a radar receiver and each object.